Methods of describing a feature of an image have been widely applied in the fields of image retrieval, image merging, object detection and identification, robot scenario positioning and an analysis of video contents, and a method for describing a local feature of an image is significantly advantageous over the rest of the methods of describing a feature of an image because it is robust against a complex background and an obscured environment and it requires no object segmentation.
At present Scale Invariant Feature Transform (SIFT) has been a feature description algorithm with high robustness commonly accepted in the art, and also numerous improved SIFT-based algorithms have emerged, for example, algorithms of Principle Component Analysis (PCA)-SIFT, Colored SIFT (CSIFT), Gradient Location Orientation Histogram (GLOH) and Speeded Up Robust Features (SURF) have achieved a good effect, and an analysis of focuses of researches on these algorithms shows that numerous researchers have focused their researches upon improved robustness of the algorithms, particularly including geometrical invariability, color invariability and affine invariability.
However all of the improved algorithms are SIFT-based, so they have a drawback in a description of an image sensitive to a change in color of the image that the improved SIFT-based methods are performed based on a grayscale image without sufficiently taking color information into account, and there are some objects with some approximate local features of their grayscale images, so these pixels may be matched in error in these methods represented by SIFT.
In summary, the existing SIFT-based methods of describing a feature of an image are performed based on a grayscale image, but a change in color of the image may impose a great influence upon the grayscale value of the image, so there may be a considerable error in a description of the image of the same contents but with a significant change in color.